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$xhtml = array(
	'<{title}>' => 'Nearly done',
	'takedown' => '2017-11-01',
	'<{body}>' => <<<END
<img src="/img/CC_BY-SA_4.0/y.st./weblog/2018/10/11.jpg" alt="What looks like a charcoal-production plant" class="framed-centred-image" width="649" height="480"/>
<section id="drudgery">
	<h2>Drudgery</h2>
	<p>
		My discussion posts for the day:
	</p>
	<blockquote>
		<p>
			When I first read the discussion assignment, I figured there was no good reason for researchers to use a normal distribution in place of the correct distribution.
			I was quite surprised to learn in from the reading material that normal distributions make great approximations when the type of distribution isn&apos;t known.
			Because normal distributions are able to approximate many other distributions decently well, normal distributions are a great tool for when the actual distribution is for some reason not known (Yakir, 2011).
		</p>
		<p>
			For example, in a population of flowers, there may be various petal lengths, like the example we looked at in a past unit.
			A combination of factors may lead to the various petal lengths, so finding the exact distribution model that causes the observed data would be difficult if not outright impossible by today&apos;s means.
			In such a case, a normal distribution can be used to explain the observed data and predict future data.
		</p>
		<p>
			On the other hand, sometimes the exact distribution <strong>*is*</strong> known.
			In these cases, the distribution tends to be found separately from the observed data, due to probabilities not coming to fruition perfectly in the data.
			For example, if you have a fair coin and toss it one thousand times, chances are, you won&apos;t get an even number of heads and tails.
			Rather, one side will come up slightly more than the other, and which side comes up more will more than likely change each time you run the experiment.
			However, separately from the observed data, you know from the properties of the coin that the chances are fifty percent.
			In such well-understood cases, where the distribution is known because it can be found separately from the data, the correct distribution model should be used instead of the normal distribution.
			There&apos;s no point in using an approximation when you have the real distribution to work with instead.
		</p>
		<p>
			There are also distributions the normal distribution doesn&apos;t approximate well, such as the exponential distribution (Yakir, 2011).
			The book also tells us that a normal distribution doesn&apos;t approximate other distributions under certain conditions.
			In such cases, the observed data can&apos;t be made fit the normal distribution in any way, so the normal distribution should not be used.
			For example, it doesn&apos;t approximate the binomial distribution well in cases in which a small number of trials are used in the binomial to be approximated (Yakir, 2011).
			In these cases, the normal distribution&apos;s approximation is too far off to be very useful.
			Continuity correction should be applied in cases in which a discreet distribution is to be approximated, which can help even in cases in which the number of trials is small.
			Likewise, if the probability of a successful trial is too low or too high, a normal distribution won&apos;t approximate it well (Yakir, 2011).
			This sort of approximation is better-suited for more even odds.
		</p>
		<div class="APA_references">
			<h3>References:</h3>
			<p>
				Yakir, B. (2011, March). Introduction to Statistical Thinking (With R, Without Calculus). Retrieved from <a href="https://my.uopeople.edu/mod/resource/view.php?id=155119"><code>https://my.uopeople.edu/mod/resource/view.php?id=155119</code></a>
			</p>
		</div>
	</blockquote>
	<p>
		I got most of my initial discussion post for the week done for my other course too, though before I submit it, I need to finish the reading material so I can tie it in in whatever ways are relevant.
	</p>
</section>
<section id="edge">
	<h2>Beyond the edge</h2>
	<p>
		I&apos;ve been wanting to try the food at this vegan restaurant I pass by every week on my way to the $a[EUGLUG] meetings, but I always forget until I pass by again, when I haven&apos;t left myself enough time to stop.
		Today, I remembered to leave home a bit early.
		It turned out to be a buffet, which I guess was okay, though not the sort of thing I&apos;m into.
		The pasta there was good though.
	</p>
	<p>
		Being at a buffet, dinner didn&apos;t take as long as planned, so I arrived at the meeting location early.
		I couldn&apos;t exactly go in just yet, so I headed to the top of that bridge I&apos;d mentioned in a mast entry as being the edge of my own world.
		I had time to kill, so I might as well satisfy my curiosity as to what lies beyond that point.
		It turns out there&apos;s a junk yard and what looks like a charcoal-production plant.
		Not overly-exciting, but now I know.
	</p>
</section>
<section id="include.d">
	<h2><code>include.d</code></h2>
	<p>
		I got a lot of work on the debug code done tonight.
		I have three major sections left to test.
		One is the generic code used for $a[URI]s with a known scheme.
		One is my hack compiler.
		And the final one is a function I had been using to convert $a[DNS]-based hidden service blocks into $a[IP]-address-based hidden service blocks.
		That code on that last one is so messy though in its implementation.
		I think I might need to rewrite it in a pretty big way, even changing the $a[API], before I bother testing it.
	</p>
</section>
END
);
